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Abstract

Mind-sets (aka implicit theories) are beliefs about the nature of human attributes (e.g., intelligence). The theory holds that individuals with growth mind-sets (beliefs that attributes are malleable with effort) enjoy many positive outcomes—including higher academic achievement—while their peers who have fixed mind-sets experience negative outcomes. Given this relationship, interventions designed to increase students’ growth mind-sets—thereby increasing their academic achievement—have been implemented in schools around the world. In our first meta-analysis (k = 273, N = 365,915), we examined the strength of the relationship between mind-set and academic achievement and potential moderating factors. In our second meta-analysis (k = 43, N = 57,155), we examined the effectiveness of mind-set interventions on academic achievement and potential moderating factors. Overall effects were weak for both meta-analyses. However, some results supported specific tenets of the theory, namely, that students with low socioeconomic status or who are academically at risk might benefit from mind-set interventions.

Keywords

mind-set implicit theories education academic achievement open data

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References

Alferes

V. R.

(2012). Methods of randomization in experimental design. Thousand Oaks, CA: Sage.

Becker

(1986). Correcting the point-biserial correlation for attenuation owing to unequal sample size. The Journal of Experimental Education, 55, 5–8.

Bishop

D. V. M.

Thompson

P. A.

(2016). Problems in using p-curve analysis and text-mining to detect rate of p-hacking and evidential value. PeerJ, 4, Article e1715. doi:10.7717/peerj.1715

Blackwell

L. S.

Trzesniewski

K. H.

Dweck

C. S.

(2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246–263. doi:10.1111/j.1467-8624.2007.00995.x

Boaler

(2013). Ability and mathematics: The mindset revolution that is reshaping education. Forum, 55, 143–152.

Borenstein

Hedges

Higgins

Rothstein

(2005). Comprehensive Meta-Analysis (version 2) [Computer software]. Englewood, NJ: Biostat.

Burnette

J. L.

Finkel

E. J.

(2012). Buffering against weight gain following dieting setbacks: An implicit theory intervention. Journal of Experimental Social Psychology, 48, 721–725. doi:10.1016/j.jesp.2011.12.020

Burnette

J. L.

O’Boyle

E. H.

VanEpps

E. M.

Pollack

J. M.

Finkel

E. J.

(2013). Mind-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139, 655–701.

Cheung

S. F.

Chan

D. K.-S.

(2004). Dependent effect sizes in meta-analysis: Incorporating the degree of interdependence. Journal of Applied Psychology, 89, 780–791.

10.

Cheung

S. F.

Chan

D. K.-S.

(2008). Dependent correlations in meta-analysis: The case of heterogeneous dependence. Educational and Psychological Measurement, 68, 760–777. doi:10.1177/0013164408315263

11.

Claro

Paunesku

Dweck

C. S.

(2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences, USA, 113, 8664–8668. doi:10.1073/pnas.1608207113

12.

Dweck

C. S.

(2000). Self-theories: Their role in motivation, personality, and development. East Sussex, England: Psychology Press.

13.

Dweck

C. S.

(2006). Mindset: The new psychology of success. New York, NY: Random House.

14.

Dweck

C. S.

(2008, Winter). Brainology: Transforming students’ motivation to learn. Independent School. Retrieved from https://www.nais.org/magazine/independent-school/winter-2008/brainology/

15.

Dweck

C. S.

(2012). Mindsets and human nature: Promoting change in the Middle East, the schoolyard, the racial divide, and willpower. American Psychologist, 67, 614–622. doi:10.1037/a0029783

16.

Dweck

C. S.

(2016). The remarkable reach of growth mind-sets. Scientific American Mind, 27, 36–41. doi:10.1038/scientificamericanmind0116-36

17.

Dweck

C. S.

Chiu

C. Y.

Hong

Y. Y.

(1995). Implicit theories and their role in judgments and reactions: A word from two perspectives. Psychological Inquiry, 6, 267–285.

18.

Egger

Smith

G. D.

Schneider

Minder

(1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315, 629–634.

19.

Eisenberg

(2005, November 6). How to help them succeed. Time. Retrieved from http://content.time.com/time/magazine/article/0,9171,1126743,00.html

20.

Hattie

Biggs

Purdie

(1996). Effects of learning skills interventions on student learning: A meta-analysis. Review of Educational Research, 66, 99–136.

21.

Hox

(1998). Multilevel modeling: When and why. In Balderjahn

Mathar

Schader

(Eds.), Classification, data analysis, and data highways (pp. 147–154). Berlin, Germany: Springer.

22.

Hunter

J. E.

Schmidt

F. L.

(1990). Dichotomization of continuous variables: The implications for meta-analysis. Journal of Applied Psychology, 75, 334–339. doi:10.1037/0021-9010.75.3.334

23.

Kish

(1965). Survey sampling. New York, NY: Wiley.

24.

Melby-Lervåg

Redick

T. S.

Hulme

(2016). Working memory training does not improve performance on measures of intelligence or other measures of “far transfer”: Evidence from a meta-analytic review. Perspectives on Psychological Science, 11, 512–534. doi:10.1177/1745691616635612

25.

Mendoza-Denton

Kahn

Chan

(2008). Can fixed views of ability boost performance in the context of favorable stereotypes? Journal of Experimental Social Psychology, 44, 1187–1193. doi:10.1016/j.jesp.2008.03.005

26.

Moher

Liberati

Tetzlaff

Altman

D. G.

& The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Medicine, 6(7), Article e1000097. doi:10.1371/journal.pmed1000097

27.

Paul

A. M.

(2013, June 11). The science of smart: Eight ways of looking at intelligence. PBS. Retrieved from http://www.pbs.org/wgbh/nova/blogs/secretlife/blogposts/the-science-of-smart-eight-ways-of-looking-at-intelligence/

28.

Paunesku

Walton

G. M.

Romero

Smith

E. N.

Yeager

D. S.

Dweck

C. S.

(2015). Mind-set interventions are a scalable treatment for academic underachievement. Psychological Science, 26, 784–793. doi:10.1177/0956797615571017

29.

Protzko

(2015). The environment in raising early intelligence: A meta-analysis of the fadeout effect. Intelligence, 53, 202–210.

30.

Raizada

R. D.

Kishiyama

M. M.

(2010). Effects of socioeconomic status on brain development, and how cognitive neuroscience may contribute to levelling the playing field. Frontiers in Human Neuroscience, 4, Article 3. doi:10.3389/neuro.09.003.2010

31.

Rattan

Savani

Chugh

Dweck

C. S.

(2015). Leveraging mindsets to promote academic achievement: Policy recommendations. Perspectives on Psychological Science, 10, 721–726. doi:10.1177/1745691615599383

32.

Romero

Master

Paunesku

Dweck

C. S.

Gross

J. J.

(2014). Academic and emotional functioning in middle school: The role of implicit theories. Emotion, 14, 227–234. doi:10.1037/a0035490

33.

Rosenthal

(1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86, 638–641.

34.

Simmons

J. P.

Simonsohn

(2017). Power posing: P-curving the evidence. Psychological Science, 28, 687–693.

35.

Simonsohn

Nelson

L. D.

Simmons

J. P.

(2014a). P-curve: A key to the file-drawer. Journal of Experimental Psychology: General, 143, 534–547.

36.

Simonsohn

Nelson

L. D.

Simmons

J. P.

(2014b). p-curve and effect size: Correcting for publication bias using only significant results. Perspectives on Psychological Science, 9, 666–681.

37.

Smith

(2014, March 17). Does teaching kids to get ‘gritty’ help them to get ahead? NPR. Retrieved from https://www.npr.org/sections/ed/2014/03/17/290089998/does-teaching-kids-to-get-gritty-help-them-get-ahead

38.

Sterne

J. A. C.

Sutton

A. J.

Ioannidis

J. P. A.

Terrin

Jones

D. R.

Lau

, . . . Higgins

J. P. T.

(2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. British Medical Journal, 343, Article d4002. doi:10.1136/bmj.d4002

39.

van Aert

R. C. M.

Wicherts

J. M.

van Assen

M. A. L. M

. (2016). Conducting meta-analyses based on p values: Reservations and recommendations for applying p-uniform and p-curve. Perspectives on Psychological Science, 11, 713–729.

40.

Walton

G. M.

Cohen

G. L.

(2003). Stereotype lift. Journal of Experimental Social Psychology, 39, 456–467. doi:10.1016/S0022-1031(03)00019-2

41.

Williams

(2012, May). Moderator analyses: Categorical models and meta-regression. Paper presented at the annual Campbell Collaboration Colloquium, Copenhagen, Denmark.

42.

Yeager

D. S.

Dweck

C. S.

(2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47, 302–314. doi:10.1080/00461520.2012.722805

43.

Yeager

D. S.

Walton

G. M.

(2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81, 267–301. doi:10.3102/0034654311405999

44.

Yeager

D. S.

Walton

Cohen

G. L.

(2013). Addressing achievement gaps with psychological interventions. Phi Delta Kappan, 94(5), 62–65.

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To What Extent and Under Which Circumstances Are Growth Mind-Sets Important to Academic Achievement? Two Meta-Analyses

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